Search Results (1,793)

Ebola virus disease (EVD) remains one of the most severe viral hemorrhagic fevers, with recurrent outbreaks challenging global healthcare systems. This narrative review traces the evolving epidemiology of EVD from the first recognized outbreaks in 1976 to the ongoing 2026 Bundibugyo virus public health emergency in the Democratic Republic of the Congo (DRC) and Uganda. Over nearly five decades, the recognized range of Ebola outbreak contexts and amplification mechanisms has broadened considerably: rural zoonotic spillovers remain the predominant mode of emergence, but the scale and reach of subsequent transmission increasingly depend on where introductions occur, on delays in detection, on population mobility, on ecological disruption, on armed conflict, on healthcare-associated transmission, and on viral persistence in survivors. Major advances in molecular epidemiology and genomic surveillance have improved outbreak investigation, enabling real-time transmission reconstruction and detection of survivor-linked resurgence. Vaccination, particularly ring vaccination with rVSV-ZEBOV, has shown high effectiveness against Zaire ebolavirus, yet vaccine equity gaps and the absence of licensed vaccines for Sudan virus and Bundibugyo virus remain critical vulnerabilities, though new candidate vaccines and therapeutics for Bundibugyo virus entered clinical evaluation in mid-2026. Artificial intelligence and digital technologies, including AI-assisted early warning systems, portable sequencing, drones, blockchain, and mobile health platforms, offer promising tools for outbreak preparedness, but robust evidence of their real-world operational impact during filovirus outbreaks remains limited, and their deployment in low-resource settings faces substantial barriers related to infrastructure, literacy, data costs, and governance. Integrated preparedness frameworks that combine ecological surveillance, resilient healthcare systems, community engagement, and international coordination under a One Health umbrella are increasingly viewed as a strategic necessity. The 2026 Bundibugyo outbreak reaffirms that despite decades of lessons, structural weaknesses in surveillance, response timeliness, and community trust continue to recur. Sustainable, multi-year financing, diversified vaccine platforms, regional manufacturing, and local co-design of digital tools are essential to translate lessons into lasting change. Preparedness is best understood as a continuous process rather than a reactive state. Full article

Canopy occlusion, dynamic vegetation, structural degeneracy, and implicit terrain risks make stable localization and task-adaptive planning difficult for resource-constrained orchard robots. This paper proposes CitraNav, a lightweight navigation method for global navigation satellite system (GNSS)-denied orchards. It separates stable geometric evidence for localization from short-lived semantic evidence for planning, preventing semantic observations from accumulating in the global map. For localization, a hierarchical voxel map selects its resolution according to local structure and light detection and ranging (LiDAR) sampling characteristics, while cross-frame reliability and observability constraints suppress updates from transient vegetation and weakly observable directions. For planning, synchronized color and depth observations form a local semantic risk point cloud. A model predictive path integral (MPPI) planner combines task-dependent semantic costs with exact-footprint collision checking against currently detected obstacles. In simulation, CitraNav achieved a mean translational localization root mean square error (RMSE) of 0.075 m. Compared with geometric point-cloud planning, semantic planning reduced the collision rate by 71.4% and increased weed coverage 4.72-fold. Across 14 real-world sequences spanning farm-road, lawn, forest, and orchard environments, CitraNav achieved mean translational and heading RMSEs of 0.151 m and 1.13°, respectively, while using 72.3–87.4% fewer geometric map cells than the comparison methods. The complete perception–planning pipeline operated at 20.3–32.7 frames per second on an edge platform. These results suggest that CitraNav offers a balanced approach to localization stability, task-adaptive planning, and computational efficiency in complex orchard navigation. Full article

Background: Dysregulated cell migration leading to metastasis remains the primary cause of cancer-related mortality. It has been challenging to understand how cells regulate migration. We have previously created the first selective inhibitor of cell migration, KBU2046. Here, we use it as a probe to identify regulatory processes. Methods: Metastatic and primary human prostate cancer cells were treated for different times and at different concentrations with KBU2046. Immunofluorescent microscopy examined protein localization in cells. Label-free mass spectrometry (MS) was performed on total cell proteins, Tandem Mass Tag (TMT) labeling MS was used on membrane fractions, and temporal phosphoproteomic profiling was performed. Results were analyzed with a suite of bioinformatic tools. Results: KBU2046-induced migrastasis is associated with the accumulation of activated integrin β1 into focal adhesions. Whole-cell proteomics demonstrated suppression of processes that mediate intracellular protein trafficking and increases in mitochondrial energy-generation signatures. Evaluation of the membrane fraction identified increases in membrane repair and maintenance processes and decreases in those that drive motility. Temporal- and concentration-dependent phosphoproteomic profiling revealed that KBU2046 initiates a dynamic, cascading sequence of transient signaling waves rather than a static block. Conclusions: KBU2046-induced migrastasis appears to operate through spatial decoupling rather than structural degradation. By restricting the intracellular trafficking machinery required for receptor recycling, KBU2046 limits focal adhesion turnover, providing, in PC3 prostate cancer cells, a correlative framework to inhibit metastatic dissemination independent of direct cytotoxicity. Full article

Log parsing converts raw system logs into structured templates and is a key preprocessing step for Artificial Intelligence for IT Operations (AIOps). Existing parsers face a practical trade-off: rule-based methods offer high throughput but limited adaptability across heterogeneous log sources, whereas Large Language Model (LLM)-based parsers achieve broader semantic coverage at the cost of inference latency, privacy exposure, and cloud dependency. This paper presents FlanBC, a log parsing framework that formulates template extraction as a BIO (Beginning, Inside, Outside) sequence-labeling task and integrates a Flan-T5 semantic encoder, Bidirectional Long Short-Term Memory (BiLSTM) layers for local sequential modeling, and a Conditional Random Field (CRF) decoder for structured label prediction. Log-specific preprocessing and a subword-to-token alignment mechanism adapt the general-purpose encoder to semi-structured log data. A layer-freezing strategy reduces the number of parameters updated during training. The framework supports local inference without external API dependency. Experiments on three benchmark datasets from LogHub (HDFS, BGL, OpenStack) under a supervised random-split setup evaluate parsing accuracy, training efficiency, statistical stability across random seeds, and component contributions. FlanBC achieves a Group Accuracy of 99.32% on HDFS and 98.47% on BGL, with an inference throughput of 700+ logs/s on a consumer-grade GPU. On OpenStack, performance is lower (GA = 92.54%), reflecting the challenge that diverse natural-language-like logs pose for compact encoder-based models. Under a stricter template-disjoint split that prevents template overlap between training and test sets, FlanBC achieves an average Group Accuracy of 91.14%, indicating that the model generalizes to unseen templates beyond in-distribution recognition. Ablation results indicate that the semantic encoder, BiLSTM module, and CRF decoder each contribute to prediction accuracy. These findings suggest that domain-adapted semantic encoders combined with structured decoding offer a practical accuracy–efficiency balance for log parsing in settings where local, cloud-free inference is preferred. Full article

Computational drug repurposing increasingly integrates chemical, biological, omics, network, text, and clinical data through deep learning. This structured narrative review examines how such modalities are encoded, aligned, and fused. We organize representative studies into four mechanism-centered families: heterogeneous-graph neural networks, multimodal knowledge-graph embeddings, pretrained language/sequence model-based cross-modal alignment, and multi-view or reconstruction-based fusion. Direct drug–disease association and repurposing studies form the core evidence; drug–target interaction, drug–drug interaction, target-identification, molecular-pretraining, and drug–microbe studies are treated as adjacent methodological evidence. We compare architectures, evaluation settings, failure modes, and evidence levels across oncology, neurology, infectious, and rare diseases. Practical guidance covers leakage-aware random, cold-start, temporal, and cluster-based evaluation; an actionable reproducibility checklist; and a scenario-based model-selection framework. We distinguish computational prioritization, docking, preclinical, retrospective clinical, and prospective evidence, and examine data sparsity, uncertain negatives, missing or noisy modalities, interpretability, and translational limitations. Future priorities include temporal and causal evaluation, external and multi-center validation, federated learning, and emerging therapeutic modalities. Multimodal fusion can improve complementary representation, but its value depends on task definition, data quality, evaluation design, and independent validation. Full article

Intelligent monitoring systems in domains such as renewable energy, electrical grid management, and environmental sensing continuously generate high-dimensional multivariate time-series data characterized by non-stationarity and multi-scale temporal dependencies. Accurate long-term forecasting of system parameters is essential for proactive maintenance, operational scheduling, and cost reduction, yet many existing models rely solely on numerical sequences and lack mechanisms to incorporate higher-level contextual information. This study proposes a multimodal long-term forecasting framework that integrates frequency-aware signal decomposition with semantic-enhanced representation learning. The framework comprises four components: (1) a wavelet-based feature extraction module that captures multi-scale periodic patterns through energy-guided frequency selection; (2) a semantic feature extraction module that encodes statistical summaries of the input into language embeddings via a pretrained language model; (3) a cross-attention fusion module that dynamically aligns temporal and semantic representations; and (4) a multi-scale MLP ensemble for robust prediction. Experiments on three benchmark datasets—Solar Power, ETTh1, and Weather—show that the framework achieves competitive accuracy against strong baselines, including PatchTST and iTransformer, with its strongest results on data exhibiting complex multi-scale seasonal structure, where it attains the second-best mean squared error on the Weather dataset. A controlled ablation isolating the pretrained embedding from a direct numerical encoding of the same statistics indicates a small, dataset-specific benefit that is comparable in magnitude to seed-to-seed variation. Overall, the proposed framework provides a modular and interpretable architecture combining frequency-aware and semantic-aware processing for intelligent system management. Full article

Carbon-fiber-reinforced polymer (CFRP) composites are widely used in lightweight aerospace structures because their mechanical performance can be adapted to different structural requirements through appropriate manufacturing conditions. This study investigates the influence of curing temperature applied using temperature-controlled heated molds on the mechanical and thermo-mechanical behavior of vacuum-infused CFRP laminates manufactured with an IN2 epoxy infusion resin. Laminates were cured at room temperature (25 °C) and at 40, 50, 60, and 70 °C using heated molds. Their performance was evaluated by tensile testing, three-point bending, and heat deflection temperature (HDT) measurements. The highest tensile strength (675.06 MPa) was obtained for laminates cured at 40 °C, whereas increasing the curing temperature beyond this value did not provide further improvement in tensile performance. The highest flexural stress at the first peak (983.36 MPa) and flexural modulus (67.17 GPa) were obtained for laminates cured at 70 °C, while the highest energy absorption during bending (0.57 J) was measured for laminates cured at 40 °C. The HDT increased from 59.77 °C for room-temperature curing to 87.60 °C for laminates cured at 70 °C, indicating improved thermo-mechanical stability with increasing curing temperature. The results indicate that no single curing temperature simultaneously maximized the tensile, flexural, and thermo-mechanical properties. Instead, the optimum curing temperature depended on the specific mechanical and thermo-mechanical requirements of the intended application. The results further indicate that controlling the temperature of heated molds during manufacturing provided a practical approach for tailoring the mechanical and thermo-mechanical performance of CFRP laminates without modifying the reinforcement architecture, laminate stacking sequence, or constituent materials. Full article

Multi-parametric industrial time-series forecasting is important for predictive monitoring, soft sensing, and decision support in complex process industries. This study proposes a process-informed hybrid ARX-residual deep sequence architecture for short-horizon multi-output forecasting of fluid catalytic cracking unit (FCCU) product yields. The model combines a frozen linear ARX branch with causal temporal convolution, a three-layer GRU, causal self-attention, additive attention pooling, residual gating, and a consistency-regularized multi-output objective. Experiments were performed on an open simulated FCCU benchmark containing 20,160 one-minute observations from seven normal and disturbed operating scenarios. The leakage-controlled pipeline uses scenario-wise chronological splitting, train-only median imputation and standardization, exclusion of fault-timing metadata and direct algebraic target components, and 30 min causal windows for five-minute-ahead forecasting. Across ten prespecified random seeds, the proposed model achieved a historical-test RMSE of 0.022809 $\pm$ 0.000116, compared with 0.022751 for ARX-like Ridge. Dependence-aware statistical analyses did not establish a consistent statistical advantage for either model across the evaluated runs. Rolling-origin evaluation likewise showed similar point accuracy, while leave-one-scenario-out evaluation revealed severe extrapolation failure for the unseen pressure-drop scenario. The results support the reproducibility of the proposed architecture and the importance of the ARX-based dynamic structure, but they do not establish the statistical superiority of the full deep residual architecture. Independent industrial data or newly prespecified simulation trajectories are required for confirmatory external validation. Full article

Offline handwritten word recognition has progressed from whole-word classification to sequence transcription, yet many systems depend on large annotated corpora and exploit lexical regularities over explicit character evidence. This paper presents an alternative formulation for Arabic offline handwritten word recognition, treating characters as spatial objects detected via a Faster Region-Based Convolutional Neural Network rather than symbols generated by a one-dimensional decoder. We construct and release a character-level annotated subset of 2153 handwritten word images from a standard Arabic benchmark, exporting matched detection, sequence, and word-class labels. We also introduce an open-source subword exchange toolkit that creates a controlled structural-generalization benchmark by swapping subwords while preserving handwriting style. Experiments compare the proposed detector against whole-word and sequence-based baselines on both the original held-out split and the perturbed benchmark. Results show sequence models degrade sharply under structural recombination, whereas the proposed detector remains stable, achieving a 26.56% character error rate and 70.0% word accuracy on the perturbed benchmark. These findings demonstrate that explicit character localization provides a robust, data-efficient alternative for Arabic handwritten text recognition in low-resource settings. Full article

Collagen functional fragments retain selected activities of parent collagens while allowing greater flexibility in sequence design and precision fermentation. Although recent reviews have covered recombinant collagen production technologies, expression platforms, purification strategies, quality control, and biomedical applications, fragment selection, host–process matching, production, and characterization have received less integrated attention. This review focuses primarily on collagen-derived functional fragments, while collagen-mimetic peptides and collagen-like proteins are discussed as related design systems. The biological basis for fragmentation includes receptor-recognition motifs, matrikines and matricryptins, and basement membrane-derived fragments. The review further examines how motif context, Gly-X-Y organization, stabilizing sequence features, protease susceptibility, post-translational modification requirements, and host compatibility influence fragment stability, expression performance, production feasibility, and product integrity. Microbial production using Escherichia coli, Komagataella phaffii, and Saccharomyces cerevisiae is discussed from the perspectives of construct–host matching, secretory or intracellular production, prolyl 4-hydroxylase configuration, fermentation optimization and scale-up, and product characterization. Finally, we discuss AI-assisted, quality-guided design-build-test-learn workflows that integrate computational prediction, curated structural, extracellular-matrix, interaction, and protease resources, two-tier candidate evaluation, and format-appropriate experimental testing to support iterative sequence, host, and process optimization. The development of collagen functional fragments therefore depends on coordinated optimization of biological function, molecular design, microbial host performance, fermentation processes, and product characterization. Full article

The invasive golden apple snail (Pomacea canaliculata) threatens rice agroecosystems, yet its direct density-dependent effects on flooded paddy soil biogeochemistry and bacterial communities remain unclear. We established flooded soil microcosms with four snail densities (0, 2, 4, and 6 snails/box) for 20 days, without external food inputs. Soil dissolved organic carbon (DOC), ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and the activities of β-glucosidase, N-acetyl-β-D-glucosaminidase, urease, and dehydrogenase were measured, and bacterial communities were characterized by full-length 16S rRNA gene amplicon sequencing. Snail density was significantly and positively related to all three nutrient variables and all four enzyme activities. The dominant bacterial phyla and genera remained stable, and bacterial α-diversity changed little among treatments, despite a small but significant increase in Simpson diversity in the high-density treatment. PERMANOVA detected significant differences in overall bacterial community structure among density treatments, while environmental fitting identified DOC, urease, and dehydrogenase as variables significantly associated with community variation. These findings indicate that living golden apple snails can alter nutrient availability, soil biochemical activity, and bacterial community organization in flooded paddy soil, revealing a belowground pathway through which this invader may influence paddy ecosystem functioning. Full article

Peak secant stiffness degradation in reinforced concrete (RC) columns governs how lateral stiffness is redistributed and where deformation concentrates during repeated earthquake loading. Fixed stiffness-reduction factors and prescribed degradation functions cannot simultaneously account for member properties and deformation demand. This study develops a hierarchical framework for predicting the deformation-dependent peak secant stiffness of rectangular RC columns. Separate specimen-level models estimate the first-reference peak secant stiffness, K₀, and the drift capacity, θ_u, from mechanical descriptors. A physics-guided cumulative sequence model then predicts the normalized stiffness-degradation path, with deformation demand normalized by the predicted drift capacity, θ_u. Non-negative degradation increments ensure bounded, monotonic stiffness loss. On the test set, the selected K₀ predictor achieved R² = 0.935, MAE = 4.098 kN/mm, and RMSE = 7.666 kN/mm; the selected θ_u predictor achieved R² = 0.927, MAE = 0.346 percentage points, and RMSE = 0.512 percentage points. With normalization based on predicted drift capacity, the degradation submodel achieved R² = 0.9272, MAE = 0.0542, and RMSE = 0.0788, substantially outperforming constant, linear, exponential, and power-law global functions. At frame level, the predicted $\hat{\mathrm{K}}$₀, $\hat{\mathsf{\theta }}$_u, and degradation ratio are incorporated into an equivalent secant-stiffness procedure. Column shear is obtained directly from the updated stiffness and interstory displacement, without a separate strength-degradation law. Comparisons with OpenSeesPy fiber-frame analyses of 12 two-, three-, and four-story frames yielded mean errors of 8.26–21.88% for base shear, 11.42–24.54% for story shear, and 10.19–23.51% for column shear; the mean absolute error in structural stiffness ratio ranged from 0.019 to 0.068. These comparisons establish a numerical consistency benchmark for the adopted frame configurations and modeling assumptions. The method is intended for stiffness-based peak-response assessment, not as a complete hysteretic constitutive model. Full article

Adeno-associated virus (AAV)-based massively parallel reporter assays (MPRA) have become an important platform for large-scale functional characterization of regulatory DNA elements. However, plasmids carrying AAV inverted terminal repeats (ITRs) are intrinsically unstable during propagation in Escherichia coli, potentially compromising library integrity before viral packaging. Although ITR-associated recombination has been recognized, the influence of cloning-site position relative to the ITR on plasmid stability has not been systematically investigated. Here, we examined the relationship between cloning-junction proximity to AAV2 ITRs and plasmid recombination using an AAV-MPRA reporter plasmid. We compared four restriction-ligation cloning strategies utilizing restriction sites at defined distances (4–543 bp) from the nearest ITR while preserving ITR integrity, and one strategy in which the ITR itself was disrupted. We observe that plasmid recombination exhibited a pronounced distance dependence. Constructs with ligation junctions located 4, 41, and 182 bp from an intact ITR showed recombination frequencies of 82.5%, 60%, and 20%, respectively, whereas a 0% recombination frequency was detected when the nearest ITR was positioned 543 bp from the cloning junction. In contrast, cleavage within ITR reduced recombination to 15%, demonstrating that preservation of the intact ITR secondary structure is required for efficient recombination. Whole-plasmid sequencing confirmed recurrent large-scale deletions in which the expression cassette and the downstream R-ITR were removed while the L-ITR and plasmid backbone were retained, consistent with preferential processing of the intact L-ITR region. These findings identify cloning-site proximity to an intact AAV ITR as a major determinant of plasmid stability during bacterial propagation and demonstrate that substantial loss of correctly assembled constructs can occur before AAV production. The results have direct implications for the design of AAV-based MPRA libraries and support positioning cloning sites as far as practical from the nearest ITR, together with routine validation of plasmid integrity prior to viral packaging. Full article

Background/Objectives: Adeno-associated virus (AAV) capsids undergo pH-dependent conformational gating at the 5-fold symmetry pore, but how these structural dynamics shape serotype-specific behavior and affinity-ligand recognition remains unclear, particularly for the clinically important serotypes AAV8 and AAV9. This study aimed to establish a pH-resolved structural framework linking 5-fold pore dynamics to peptide-ligand recognition and to translate this framework into sequence-based design principles for affinity capture of gene therapy vectors. Methods: AAV8 and AAV9 5-fold capsid assemblies were subjected to 500 ns molecular dynamics simulations under acidic (pH 5), neutral (pH 7), and basic (pH 9) conditions, with analysis of pore volume, inter-residue contact networks, electrostatic potential, and solvent-accessible surface area. In parallel, affinity chromatography using three mixed-mode peptide ligands (RVVAVYRI, TTFRAHHI, and TYHHHHII) was performed on clarified HEK293 lysates containing AAV8 or AAV9, with capsid yield, host-cell-protein clearance, and transduction activity assessed by ELISA, SEC-HPLC, and flow-cytometry-based transduction assays. Results: AAV8 displayed a heterogeneous, bimodal pore conformational landscape at pH 7, whereas AAV9 exhibited a discrete gate-like transition with maximal pore constriction at physiological pH; both serotypes showed pore-proximal contact remodeling with distinct network topologies. Experimentally, TYHHHHII achieved the highest selectivity for genome-containing capsids at pH 7, with transduction activity enrichment factors of 2.82 (AAV8) and 5.61 (AAV9), while TTFRAHHI provided the broadest operational pH range for bulk capsid recovery. Conclusions: These findings establish a structural framework linking pH-dependent pore dynamics to affinity ligand recognition and suggest practical sequence-design rules for ligand engineering: clustered histidines for neutral-pH selectivity, Arg-containing motifs for broad-pH robustness, and aromatic or hydrophobic residues for reinforcement of capsid binding. Full article

This paper proposes a unified invariant-set-based reliable control framework for Takagi–Sugeno (T–S) fuzzy systems subject to actuator saturation and faults. The considered model incorporates both matched actuator faults and mismatched external disturbances, which provides a more realistic control setting. To address these challenges, a unified control framework is developed to systematically account for input constraints and actuator fault effects. A sequence of nested invariant ellipsoidal sets, together with corresponding set-dependent control gains, are constructed to guarantee that state trajectories starting within the designed outer invariant sets progressively converge toward a minimized target set. Based on this structure, relaxed LMI-based conditions are derived to compute both the invariant sets and the associated control laws via convex optimization. Finally, numerical examples demonstrate the effectiveness of the proposed method. Full article